Developing a Digital Twin System Based on a Physics-informed Neural Network for Pipeline Leakage Detection

As the demand for resources continues to grow, pipelines have become critical for transporting water, fossil fuels, and chemicals. Monitoring pipeline systems is essential, as leaks can lead to severe environmental damage and safety hazards. This study aims to develop a pipeline leakage detection system based on digital twin technology and Physics-Informed Neural Networks (PINNs). By embedding physical principles, such as the continuity and momentum equations derived from the Navier-Stokes equation, into the neural network's loss function, the model can predict pressure and flow dynamics with high accuracy while adhering to physical constraints. PINNs are particularly advantageous as they require minimal data, maintain physical consistency, and provide reliable interpretations, making them well-suited for addressing pipeline safety challenges. The model is designed to simulate fluid dynamics under normal operating conditions, with deviations in prediction errors signaling potential leaks. Statistical analysis of these errors is used to define control limits, establish rejection regions, and create control charts for leak detection. The detection system is further validated using field data to ensure reliability. By combining physical modeling and neural networks, this approach enhances the accuracy and interpretability of leakage detection, laying a solid foundation for an efficient pipeline monitoring solution.